Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/14893
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAkshara P.
dc.contributor.authorShidharth S.
dc.contributor.authorKrishnan G.S.
dc.contributor.authorSowmya Kamath S.
dc.date.accessioned2021-05-05T10:15:57Z-
dc.date.available2021-05-05T10:15:57Z-
dc.date.issued2020
dc.identifier.citationACM International Conference Proceeding Series , Vol. , , p. 436 -en_US
dc.identifier.urihttps://doi.org/10.1145/3430984.3431060
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14893-
dc.description.abstractThe large-scale availability of healthcare data provides significant opportunities for development of advanced Clinical Decision Support Systems that can enhance patient care. One such essential application is automated ICD-9 diagnosis group prediction, useful for a variety of healthcare delivery related tasks including documenting, billing and insurance claims. Past attempts considered patients' multivariate lab events data and clinical text notes independently. To the best of our knowledge, ours is the first attempt to investigate the efficacy of integration of both these aspects for this task. Experiments on MIMIC-III dataset showed promising results. © 2021 Owner/Author.en_US
dc.titleIntegrating Structured and Unstructured Patient Data for ICD9 Disease Code Group Predictionen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.